Abstract
Charged particle tracking represents the largest consumer of CPU resources in high data volume Nuclear Physics (NP) experiments. An effort is underway to develop machine learning (ML) networks that will reduce the resources required for charged particle tracking. Tracking in NP experiments represent some unique challenges compared to high energy physics (HEP). In particular, track finding typically represents only a small fraction of the overall tracking problem in NP. This presentation will outline the differences and similarities between NP and HEP charged particle tracking and areas where ML learning may provide a benefit. The status of the specific effort taking place at Jefferson Lab will also be shown.
Highlights
The collaborative effort of applying machine learning (ML) tools and techniques to the challenge of tracking in nuclear physics is made up of members from both the GlueX[1] and CLAS12[2] experiments
Tracks in nuclear physics experiments at Jefferson Laboratory differ from those found in high energy physics (HEP) experiments, such as those at the Large Hadron Collider (LHC), in several distinct ways
Nuclear physics tends to deal with far fewer tracks per event; on the order of 2-4 in Nuclear Physics (NP) versus hundreds to thousands in HEP at the LHC
Summary
The collaborative effort of applying machine learning (ML) tools and techniques to the challenge of tracking in nuclear physics is made up of members from both the GlueX[1] and CLAS12[2] experiments. Tracks in nuclear physics experiments at Jefferson Laboratory differ from those found in high energy physics (HEP) experiments, such as those at the Large Hadron Collider (LHC), in several distinct ways. There exist more subtle differences between the challenges facing GlueX and CLAS12.
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